We draw on the tractable approach proposed by [1]} to compute mutual information, which approximates the inferred mutual information by adjusting the parameters of the model to obtain multiple predictions.
In this paper, we propose to calculate the uncertainty of the model prediction results with the help of several data augmentation methods. Specifically, MI \(\mathbb {I}^{(t)}[y \mid \mathbf {x}_j^Q]\)  is calculated based on the classification scores of \(m+1\)  augmented instances via Shannon entropy \(\mathbb {H}(\cdot )\) , which is shown as:
